There are at least two important categories of object loyalty definitions (wherein “object” may be a brand, company, organization, product or service). The first category is “operational” object loyalty definitions, wherein such loyalty is defined and measured by analysis of, e.g., customer purchasing behaviors. That is, since one cannot look into a customer's mind, one looks instead into the customer's shopping cart, a parts bin, or an order history. Thus, customer loyalty behavior toward an object is analyzed, according to at least one of the following operational definitions of loyalty: (a) “choosing the object on k of n opportunities or purchase occasions,” (b) “choosing the object k times in a row,” or (c) “choosing the object more often than any other.”
A second category of object loyalty definitions include definitions that provide a description of a “psychological” state of: (a) a predisposition to buy, or (b) a conditional preference, e.g., an attitude, which may be favorable or unfavorable to the object. That is, the definitions of this second category provide descriptions of the mental state(s) of a customer(s) so that one can hypothesize a framework for assessing object loyalty. A customer's attitudes, however, are based in their beliefs, wherein beliefs are descriptive thoughts about things that drive customer choice behavior. Said another way, belief connotes conviction, whereas attitude connotes action.
However, neither of the above definitions of object loyalty are satisfactory for customer loyalty, and at least as importantly, for determining how customer loyalty can be cost effectively increased. For example, for an “operationally” identified loyal customer who buys over and over again, there is no certainty that this customer is actually loyal. Not unless one knows that the purchasing choice was: (a) at least relatively unconstrained, for example, that the customer did not face costs to switch to a competing product, and (b) that the choice is made in congruence with the customer's preferences. In fact, it may be that the customer is uniformed regarding the market, and/or indifferent to competitive offerings. Moreover, for a “psychological” identified loyal customer who has a predisposition to perform a transaction with or for an object (as defined hereinabove), there is also no certainty that this customer is actually loyal. In particular, it does not mean that the customer will be more likely to perform such a transaction. To illustrate, an individual may admire a Mercedes, and say it is the best of cars, but cannot afford one. Is he/she loyal? At least from a marketing perspective probably not.
With belief and behavior comes experience. Experience, over time, creates in customers' minds a set of ideas (i.e., perceptions) about an object. Thus, the term loyalty as used herein may be described as including: (1) favorable customer perceptions built up over time, as evidenced by both belief and behavior, that induce customers to perform transactions (e.g., purchases) of, from or with the object, and (2) such favorable customer perceptions are a barrier for the customers to switch to a competing object (e.g., a competing brand, company, organization, product or service). Evaluation of such object loyalty is desirable for making informed marketing decisions regarding the object, particularly, if such evaluations can be performed cost effectively.
The equity of an object (e.g., a brand, company, organization, product or service), may be described as the aggregate loyalty of the object's customers to continue acquiring or using (service(s) and/or product(s) from) the object. Equity, then, may be considered a function of (f1) the “likelihood of repeat purchase,” which is a function of (f2) loyalty, which in turn is a function of (f3) customer satisfaction, which following from the standard satisfaction attitude research framework, is a function of (f4) the belief and importances of attribute descriptors. Said another way,Equity=f1(likelihood of repeat purchase)=f2(loyalty)=f3(satisfaction)=f4(beliefs, importances)
A company that has built substantial customer equity can do things that other companies cannot. In particular, the greater number of loyal customers, the greater degree of protection from competitive moves and from the vagaries of the marketplace. FIG. 1 illustrates this point. That is, customer loyalty may insulate a brand or product from competitive marketing activities and from external shocks, thus reducing risk (technically, the variance), increasing brand value and, ultimately, company value. In other words, high customer equity for an object reduces the ability of a competitor or event to shift the two components of loyalty, beliefs and behavior. For example, brand loyal customers may ignore or, even better, actively counter-argue competitive claims and resist their marketing actions. Brand loyal customers also resist, to some degree, competitive price promotions since the risk reduction attributable to the incumbent brand is greater than the value of the price reduction offered by the competitor.
Thus, evaluation of such object equity is desirable so that informed marketing and business decisions regarding the object can be made, particularly, if such evaluations can be performed cost effectively.
The primary focus of a marketing manager, when framing a marketing strategy for an object, in order of importance, is: (a) maintaining the object's loyal customer base, and (b) increasing the number of “new loyals.” Increasing sales can be seen as a direct result of these two strategic marketing focuses. For the first “maintenance of loyals” group, two questions arise: (1) why do such loyal customers decide to, e.g., purchase our product instead of the competition's product, and (2) what barriers exist for loyal light users to becoming heavier users. The answer to the first question defines the equity of the business. The answer to the second question gives management insight into how directly to increase sales—by minimizing the barriers for increasing customer loyalty. In particular, the techniques and/or features for attracting non-loyal customers, heavy users and light users, respectively, to become more loyal to an object is the input that a marketing manager needs for developing a strategy that increases sales. Also, attracting loyal customers of a competitive object represents yet another separate strategic issue. These key inputs, which are grounded in the ability to understand (summarize, quantify and contrast) the customer decision processes of target customer populations, provides the marketer with the insight required to optimally develop effective marketing strategy. Thus, a method and system for cost effectively answering the above two questions (1) and (2) is desirable so that informed marketing and business decisions regarding the object can be made.
Many marketers have made the realization that loyalty is key to a successful business strategy, and they have operationalized the research of loyalty in terms of customer satisfaction. In fact, customer satisfaction research is one of the largest and fastest growing areas of market research. There exist numerous specialty customer satisfaction assessment research orientations by market, e.g., for universities (Noel-Levitz incorporated herein by reference), for healthcare (Press-Ganey incorporated herein by reference), for government services (Opinion Research Corporation incorporated herein by reference) and for brand satisfaction (Burke, Inc. herein by reference). These marketing research organizations use methodologies (referred to herein as “attitudinal methodologies”) based upon a traditional attitudinal research framework directed to assessing customer attitudes. That is, they ask questions of customers regarding their beliefs as to what degree a company's product, and competitive products, possess a given set of brand and/or service descriptors (e.g., attributes) and the relative importance of these descriptors to the company's customers (and/or the competitor's customers). The analysis output by such market research, as one skilled in the art will understand, is a set of mean belief ratings for the descriptor attributes, as well as mean importance ratings which can be broken down, if desired, for the various customer segments. Moreover, the analysis output provided by these marketing research organizations provides ongoing customer tracking to assess customer attitude changes over time, so that interpretation of the mean statement customer response scores serves as a basis for strategic decision-making by the object being evaluated. As will be detailed hereinbelow, the approaches and methodologies used by these market research organizations are believed to be sub-optimal for a variety of reasons. However, before describing perceived problems with these prior art market research approaches and methodologies, examples of various marketing challenges are first provided as follows.                Consumer goods. Consider the soft drink marketplace. There are loyal customers that, regardless of small price differences, purchase and consume virtually 100% of one brand. They are satisfied with the performance of the product and what it stands for (imagery). Marketing pressures, specifically alternating weekly price promotions by the two market leaders in supermarkets, have decreased the number of loyal customers for the brand as compared to a generation ago. This reduction in loyalty translates into additional marketing and sales costs to drive revenue, which corresponds to decreased profitability.        Durable goods. Consider the automobile marketplace as recently as a generation ago. Customers were happy to wear the label reflective of their loyalty, such as “Ford” or “Buick” or “Cadillac.” This label simply meant they owned and would continue to buy their brand of car. That is, they were satisfied with the performance of the product and what it stood for (imagery). As is obvious, due to competitive (and sometimes internally counterproductive) marketing efforts, this loyalty has been greatly diminished. The result is the equity of their business, and correspondingly its profitability, has decreased.        Direct sales. Consider the recruiting and retention issues for a direct sales force (Mary Kay Cosmetics, 1989). Sales revenues are a direct function (a 0.99 correlation) of the number of active sales representatives in the marketplace. The sales force has significant turnover (non-loyalty), which is a result of dissatisfaction with the job. If the sales force can be recruited at a higher rate and will remain active longer, thereby reducing the turnover rate, the size of the sales force may increase exponentially, which translates directly into significant increases in sales revenues. The equity of the direct sales company is a function of a satisfied, loyal sales force.        Healthcare. Consider the choice of hospitals in a given geographic area. If customer-patients are satisfied, they will return for future treatments and recommend the facilities to their friends. Patient loyalty translates into continued business for the hospital. Their dissatisfaction, however, means moving their business to the competition, thereby reducing the revenue of the hospital. The equity of the hospital is a function of its satisfied, loyal customers who will continue to use its services.        Nonprofit. Consider a museum, which is financially supported to a significant degree by annual donations of its membership. Their financial contributions represent a market share across a variety of competitive nonprofit options. If the members are satisfied with the offerings and operation of the museum, they will remain loyal and continue to give. If they are not satisfied, they will decrease or cease their funding activity. In this latter case, the equity of the museum, not to mention its direct operational funds, decreases. The equity of the museum is in its loyal donor base.        Resort. Consider a country club business in a given geographic area. Satisfied customers will remain members. Dissatisfied members will seek out other options, and this translates into a lower membership, meaning lower revenues received, which in turn translates into a lesser ability to fund club operations. The result is a reduction in the equity of the country club, which is a direct function of the loyalty of its membership.        
The examples of marketing situations outlined above serve to illustrate the fact that the primary function of a market-driven strategy is to maximize the equity of an object, which translates to maximizing customer loyalty, which requires gaining an understanding of what customer (and employee) perceptions are that drive satisfaction. As the above examples of marketing challenges illustrate, maximizing or increasing the equity of an object is desirable for virtually all business enterprises. Thus, it would be desirable to have a method and system for performing market research that determines the relative weights of components or aspects of an object that will maintain and increase customer satisfaction (with respect to, e.g., predetermined target customer groups). These components or aspects, when communicated and delivered by the object, will likely increase the satisfaction level, thereby increasing loyalty, likelihood of repeat purchase, and result in increasing equity (as this term is used herein).
Attitudinal Research Framework Descriptions
Attitude models (Allport, 1935, Ref. 2. of the “References” section incorporated herein by reference) represent the prototypical, most frequently used research framework utilized in the domain of marketing research. The tripartite social psychological orientations of cognitive (awareness, comprehension, knowledge), affective (evaluation, liking) and conative (action tendency) serve as the research basis of gaining insight into the marketplace by understanding the attitudes of its customers.
Questions regarding any component, or combinations thereof, of the attitude model are regarded as attitude research. Conative, for example, refers to behavioral intention, such as a likelihood to purchase, which is prototypically asked in the following scale format for a specific product/service format (Zigmund, 1982, p. 325, Ref. 11 of the “References” section incorporated herein by reference).
□ I definitely will buy□ I probably will buy□ I might buy□ I probably will not buy□ I definitely will not buy
Therefore, if the past purchase or consumption behavior for each individual in the sample of respondents were known from another question in the survey (or consumer diary), the behavioral intention question would be used to compute the likelihood of repeat purchase.
Satisfaction (affect for the consumption and/or use experience) is typically measured using a scale such as the following for a specific product or service (Zigmund, 1982, p. 314-315, Ref. 11 of the “References” section incorporated herein by reference).
□ Very satisfied□ Quite satisfied□ Somewhat satisfied□ Neither satisfied or dissatisfied□ Quite dissatisfied□ Very dissatisfied
Attitude research is based on a theoretical model (Fishbein, 1967, Ref. 8 of the “References” section incorporated herein by reference) containing two components: one, beliefs about the product attributes of the object, and two, an evaluation of the importances of beliefs (descriptors). This theoretical relationship may be represented as:
          ⁢                                                                        A                0                            =                                                ∑                                      i                    =                    1                                    n                                ⁢                                                                  ⁢                                                      b                    i                                    ⁢                                      e                    i                                    ⁢                                                                          ⁢                  where                                                      ,                          =                            ⁢                              attitude                ⁢                                                                  ⁢                toward                ⁢                                                                  ⁢                the                ⁢                                                                  ⁢                object                                              ⁢                                                                                                        b              i                        =                        ⁢                          strength              ⁢                                                          ⁢              of              ⁢                                                          ⁢              the              ⁢                                                          ⁢              belief              ⁢                                                          ⁢              that              ⁢                                                          ⁢              object                                ⁢                                                                                        ⁢                      has            ⁢                                                  ⁢            attribute            ⁢                                                                      ⁢                                                                    ⁢            i                                                                    e            i                    =                    ⁢                      evaluation            ⁢                                                  ⁢            of            ⁢                                                  ⁢            attribute            ⁢                                                  ⁢            i                                                        n          =                    ⁢                      number            ⁢                                                  ⁢            of            ⁢                                                  ⁢            belief            ⁢                                                  ⁢            descriptors                              
Attitude toward the object (Ao), then, is a theoretical function of a summative score of beliefs (i.e., “bi”—descriptors or characteristics) multiplied by their respective importances (“ei”). Assuming this theory to hold, market researchers construct statements to obtain beliefs specific to product and/or services, such as (Peter and Olson, 1993, p. 189, Ref. 16 of the “References” section incorporated herein by reference):
How likely is it that 7UP has no caffeine?Extremely Unlikely 1 2 3 4 5 6 7 8 9 10 Extremely LikelyHow likely is it that 7UP is made from all natural ingredients?Extremely Unlikely 1 2 3 4 5 6 7 8 9 10 Extremely Unlikely
Additionally, market researchers obtain importances using scales that generally appear in the following format (Peter and Olson, 1993, p. 191, Ref. 16 of the “References” section incorporated herein by reference):
7UP has no caffeine.Very Bad −3 −2 −1 0 +1 +2 +3 Very Good7UP has all natural ingredients.Very Bad −3 −2 −1 0 +1 +2 +3 Very Good
For the three standard types of attitude scales noted above, the researcher assigns numbers (integers) to the response categories. In the cases of the behavioral intention scales and satisfaction (affect), successive integers are used such as (+2 to −2, and +3 to −3, respectively). Analysis of the data then involves computing summary statistics for each item, for the customer groups of interest.
In sum, from the perspective of marketing research, customer understanding is derived from studying the tables of summary statistics indicative of customer responses related to a combination of product and/or service customer beliefs (cognitive), corresponding customer importances (affective) with regard to key attribute descriptors, and the likelihood of acting (conative).
Difficulties with the above attitude research methodology for measurement of attitudes include individual differences in interpretation of questions, which result in a compounding of error of measurement. Detailed below are the assumptions that underlie the use of attitude models, along with examples of how error is introduced into the resulting measures.
1. Core Meanings or Terms are Commonly Understood.
                For example, when “good value” is used as a descriptor phrase to be evaluated, there could be many different interpretations, depending on each customer's definition or operationalization of the concept of value (reciprocal trade-off between price and quality).        Therefore, if the meanings of attributes, which will be used to measure beliefs and importances, differ by respondent, there is no uniformity in the responses.2. Social Demand Characteristics will not Introduce Bias.        For example, when a socially acceptable norm (positive or negative) is used, such as in the case with automobiles with the terms “prestige” or “status,” respondents consistently and significantly under report the importance of these attitude descriptors as contrasted to open-ended discussions describing their own choice behavior (Reynolds and Jamieson, 1984, Ref. 25 of the “References” section incorporated herein by reference).        
3. The Descriptor Labels on the Judgment Scales are Commonly Understood.                For example, when using word descriptors, such as “definitely” or “probably” in scale labeling, their definitions cannot be assumed to have the same meanings to each respondent.        For example, when numbers are used, especially percentages, to define the scale points, the likelihood that a common definition or meaning of the terms are held by all respondents is very unlikely.        
4. The Scales are One-dimensional.                For example, when only end-markers of scales are used, such as “good” and “bad,” this assumes these are exact opposites. It has been shown (Reynolds, 1979, Ref. 17 of the “References” section incorporated herein by reference) that a significant percentage of respondents actually use two dimensions here, namely, “good”   “not good” and “bad”   “not bad.” Similarly, “hot” and “cold” are not opposites. Rather, “hot”   “not hot” and “cold”   “not cold” represent the basis for their cognitive classifications.        If the scales are not one-dimensional, the measurements are confounded, further injecting additional error into the research data.5. The Intervals Between the Points on the Scale will be Equal.        For example, when considering the appropriate response that represents one's position on a numerical scale, the individual must mentally impose a metric—based upon the fact that the exact difference between all scale points is equal.        If the respondents do not have a precise interval metric interpretation of all scales, in particular with respect to beliefs and importances, all that exists is an ordinal ranking of scores, which would not make simple means an appropriate summary measure of central tendency.        
The above problematic assumptions have been individually discussed in virtually all psychology and marketing research textbooks. However, in reality, these issues have never been adequately addressed, especially in light of the compounding effect caused by multiple violations of the assumptions. Understanding the potential confounding effect of these assumption violations can be even more problematic to obtaining valid measures when the following not-previously-identified assumption is considered:
6. Importances are Assumed to be Independent of Beliefs. (That is, importances are distributed equally across belief scales. Denoted herein as the “uniform importances assumption”.)
                For example, if a person has a given belief level or position on an attitude scale, e.g., an attitude of “not satisfied,” what is assumed important to him/her is both: (a) some weighted composite of the importance scores across all the attribute dimensions, and (b) that these importances are somehow independent of his/her belief level. That is to say, if one asks how to increase a respondent's attitude score/satisfaction level one Δ (i.e., one scale point), the assumption that has heretofore been made is that a weighted composite of attribute scores would be needed, and regardless of the level (higher or lower) on the attitude scale, the same weighted composite is used by the person.        Asking three questions can test this uniform importances assumption. First, an “anchor” question establishing a position on the attitude scale of interest is presented to a respondent. In the example anchor question [1] immediately below, a “satisfaction” question is presented to the respondent. Following this anchor question hereinbelow are second and third questions which simply ask for the key attribute or reason that is the basis for the person's rating in question [1].        
[1]. ANCHOR. How satisfied are you with (BRAND “Y”) product/service?NOT AT ALLPERFECTLY−−−−−−−−−−0++++++++++[2]. What is the one thing that causes you to rate your satisfactionat this level? That is, why did you rate your satisfaction at (X) and not(X − 1) (one scale point lower)?[3]. What is the one thing that would increase your satisfaction byone scale level, that is, from (X) to (X + 1)?                If the assumption holds that importances are equally distributed across the scale points of the attitude scale, the most likely outcomes would be that the most important attribute would be mentioned for both questions [2] and [3] above, or alternatively, that the first and second most important attributes would be mentioned in the response for questions [2] and [3].        The conclusions from customer research using the above questioning format do not confirm the uniform importances assumption. In fact, importances are not equally distributed across such an attitude response scale. To empirically test this assumption, the above three questions [1], [2], and [3] were asked of independent samples of respondents (total of 750) across the five product/service categories mentioned previously, from durable goods to nonprofits. Analysis of the responses to questions [2] and [3] revealed that: (a) in less than two percent (2%) of the cases was the same attribute mentioned for both questions [2] and [3], and (b) the first and second most important attributes (determined by a traditional market research importance scale) combined were mentioned less than 50% of the time.        Thus, if the market research question(s) is how best to improve respondents' attitudes (e.g., satisfaction level underlying loyalty), the above attitude research methodology is believed to be flawed.        
Within the attitude research methodology, another newly discovered assumption that is also suspect is as follows:
7. Product or Service Attributes Drive Customer Decisions and should be the Primary Area of Research Focus.
                This assumption is held by all traditional attitude models and has been empirically demonstrated to be false. Research has shown (Reynolds, 1985, Ref. 18 of the “References” section incorporated herein by reference; Reynolds, 1988, Ref. 19 of the “References” section incorporated herein by reference; Jolly, Reynolds and Slocum, 1988, Ref. 14 of the “References” section incorporated herein by reference) that higher levels of abstraction beyond attributes (e.g., consequences and personal values) contribute more to understanding preferences and performance ratings than do lower-level descriptor attributes. Therefore, to gain a more accurate knowledge of the basis of customer decision-making, one must understand the underlying, personally relevant reasons beyond the descriptor attributes provided by respondents.        
Accordingly, it is desirable to have a market research method and system that provides accurate assessments of, e.g., customer loyalty, and accurate assessments of the attributes of an object that will influence customers most if changed. In particular, it is desirable that such a market research method and system is not dependent upon the above identified flawed assumptions.
The invention disclosed hereinbelow addresses the above identified shortcomings of prior art market research methods and systems, and in particular, the invention as disclosed hereinbelow provides a market research method and system that provides the desirable features and aspects recited hereinabove.